Search form

You are here

Big Data Analytics of HIV Treatment Gaps in South Carolina

July 7, 2017

Dr. Hu has collaborated with Professor Xiaoming Li (PI) in Health Promotion, Education & Behavior and other USC faculty members to secure an NIH Funded project entitled "Big Data Analytics of HIV Treatment Gaps in South Carolina: Identification and Prediction" and is funded by National Institute of Allergy and Infectious Diseases (NIAID)/NIH.

Abstract

Linkage into and retention in HIV medical care for HIV+ individuals is important for patient survival and treatment as key components of HIV prevention. National and South Carolina (SC) estimates of retention in HIV medical care are slightly above fifty percent indicating a gap in HIV treatment. With significant proportions of HIV+ individuals not receiving HIV medical care, improved outcomes of care and HIV prevention as part of national HIV/AIDs strategies remain difficult to achieve. The purpose of this study is to use novel machine learning techniques such as deep learning using neural networks to further explore, identify, characterize, and explain predictors of missed opportunities for HIV medical care utilization among all living HIV+ individuals in SC. Profiles of HIV+ individuals based on their patterns of HIV medical care seeking; how will the data gathered under supervised and unsupervised learning inform one another? behavior will be developed with concomitant identification of both gaps in HIV care and missed opportunities for reengagement into HIV care. The public health prevention value that HIV treatment brings includes improved survival and outcomes of care among HIV+ individuals as well as reduced HIV transmission. These important components form part of the overall strategy for fighting and controlling the HIV epidemic in the United States (US) and align closely with the strategic goals of reducing new HIV infections. Using state-level CD4 and Viral Load (VL) testing data available for all SC HIV+ individuals since 2004, the study will link inpatient and outpatient claims data sources and data from the state corrections database to create a unique population based dataset spanning 10 years (2004-2013). Advanced big data analytical techniques such as artificial neural networks, automated cluster analysis and decision tree analyses will be used to create person level profile patterns of health utilization behaviors and for identifying best predictors of linkage and retention in HIV medical care. Deep learning algorithms based on deep neural networks will be used to unearth hidden features/predictors of HIV medical care utilization. A predictive model useful for predicting where HIV+ individuals who are not in care will attend for routine medical care (missed opportunities) will also be developed.